Optimizing Rice Plant Disease Classification Using Data Augmentation with GANs on Convolutional Neural Networks
DOI:
https://doi.org/10.29407/intensif.v9i1.23834Keywords:
CNN, DCGAN, Image Quality Enhancement, Imbalanced Datasets, Synthetic DataAbstract
Background: Rice disease classification using CNN models faces challenges due to limited data, particularly in minority classes, and inconsistent image quality, which affect model performance. Data augmentation techniques can potentially enhance classification accuracy by improving data diversity and quality. Objective: This study evaluates the effectiveness of data augmentation techniques, specifically classical augmentation and Deep Convolutional Generative Adversarial Networks (DCGAN), in improving CNN performance for rice disease classification. Methods: A quantitative study was conducted using four CNN training scenarios: no augmentation, classical augmentation, DCGAN augmentation, and a combination of both. Model accuracy was analyzed to determine the impact of each augmentation technique. Results: The baseline CNN model achieved an accuracy of 91.88%. Classical augmentation improved accuracy by 2.56%, while DCGAN augmentation led to a 5.44% increase. The combination of classical augmentation and DCGAN yielded the highest accuracy of 98.13%. Conclusion: Data augmentation significantly enhances CNN performance in rice disease classification, with the combined approach of classical augmentation and DCGAN proving to be the most effective. These findings highlight the importance of augmentation techniques in addressing data limitations and improving classification accuracy. Future research should explore additional augmentation strategies and test the model across different datasets to further validate its effectiveness.
Downloads
References
A. S. Wibowo, Indikator Pertanian 2020. ©Badan Pusat Statistik/BPS- Statistics Indonesia, 2021.
A. B. Setiawan, A. D. Nugroho, D. T. Wiyanti, M. Yusuf, A. Maslikhatun, and P. A. Bowo, “Indonesian Food Production Challenges: Climate, Land and Industrialization,” JEJAK, vol. 16, no. 1, Mar. 2023, doi: 10.15294/jejak.v16i1.43334. DOI: https://doi.org/10.15294/jejak.v16i1.43334
N. Ngadi et al., “Challenge of Agriculture Development in Indonesia: Rural Youth Mobility and Aging Workers in Agriculture Sector,” Sustainability, vol. 15, no. 2, p. 922, Jan. 2023, doi: 10.3390/su15020922. DOI: https://doi.org/10.3390/su15020922
Statista, “Agriculture in Indonesia - statistics & facts,” Statista Research Department, 2023. [Online]. Available: https://www.statista.com/topics/7732/agriculture-industry-in-indonesia/#topicOverview
I. Hamim, B. Sipes, and Y. Wang, “Editorial: Detection, characterization, and management of plant pathogens,” Front. Plant Sci., vol. 15, Feb. 2024, doi: 10.3389/fpls.2024.1354042. DOI: https://doi.org/10.3389/fpls.2024.1354042
Y.-P. Wang et al., “Optimizing Plant Disease Management in Agricultural Ecosystems Through Rational In-Crop Diversification,” Front. Plant Sci., vol. 12, Dec. 2021, doi: 10.3389/fpls.2021.767209. DOI: https://doi.org/10.3389/fpls.2021.767209
L. Kaur and S. G. Sharma, “Identification of plant diseases and distinct approaches for their management,” Bull. Natl. Res. Cent., vol. 45, no. 1, p. 169, Dec. 2021, doi: 10.1186/s42269-021-00627-6. DOI: https://doi.org/10.1186/s42269-021-00627-6
L. Li, S. Zhang, and B. Wang, “Plant Disease Detection and Classification by Deep Learning—A Review,” IEEE Access, vol. 9, pp. 56683–56698, 2021, doi: 10.1109/ACCESS.2021.3069646. DOI: https://doi.org/10.1109/ACCESS.2021.3069646
A. Jafar, N. Bibi, R. A. Naqvi, A. Sadeghi-Niaraki, and D. Jeong, “Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations,” Front. Plant Sci., vol. 15, Mar. 2024, doi: 10.3389/fpls.2024.1356260. DOI: https://doi.org/10.3389/fpls.2024.1356260
A.-K. Mahlein et al., “Special Issue: Digital Plant Pathology for Precision Agriculture,” J. Plant Dis. Prot., vol. 129, no. 3, pp. 455–456, Jun. 2022, doi: 10.1007/s41348-022-00620-9. DOI: https://doi.org/10.1007/s41348-022-00620-9
İ. Yağ and A. Altan, “Artificial Intelligence-Based Robust Hybrid Algorithm Design and Implementation for Real-Time Detection of Plant Diseases in Agricultural Environments,” Biology (Basel)., vol. 11, no. 12, p. 1732, Nov. 2022, doi: 10.3390/biology11121732. DOI: https://doi.org/10.3390/biology11121732
B. P. Zen, I. K. A, and D. C. Fransisca, “Applications for Detecting Plant Diseases Based on Artificial Intelligence,” Sinkron, vol. 7, no. 4, pp. 2537–2546, 2022, doi: 10.33395/sinkron.v7i4.11833. DOI: https://doi.org/10.33395/sinkron.v7i4.11833
J. Yao, S. N. Tran, S. Garg, and S. Sawyer, “Deep Learning for Plant Identification and Disease Classification from Leaf Images: Multi-prediction Approaches,” Oct. 2023, [Online]. Available: http://arxiv.org/abs/2310.16273
F. Haider, A. Ahmed, and M. Rahman, “CNN-Based Rice Plant Disease Detection,” J. Plant Sci. Res., vol. 12, no. 4, pp. 301–310, 2023.
K. Haider et al., “A Comprehensive Study of Plant Disease Detection Using Deep Learning Methods,” 2023, pp. 441–458. doi: 10.1007/978-3-031-25088-0_40. DOI: https://doi.org/10.1007/978-3-031-25088-0_40
L. Budach et al., “The Effects of Data Quality on Machine Learning Performance,” Jul. 2022, [Online]. Available: http://arxiv.org/abs/2207.14529
B. Tugrul, E. Elfatimi, and R. Eryigit, “Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review,” Agriculture, vol. 12, no. 8, p. 1192, Aug. 2022, doi: 10.3390/agriculture12081192. DOI: https://doi.org/10.3390/agriculture12081192
Y. Chen, J. Pan, and Q. Wu, “Apple leaf disease identification via improved CycleGAN and convolutional neural network,” Soft Comput., vol. 27, no. 14, pp. 9773–9786, 2023, doi: 10.1007/s00500-023-07811-y. DOI: https://doi.org/10.1007/s00500-023-07811-y
D. Dablain, K. N. Jacobson, C. Bellinger, M. Roberts, and N. Chawla, “Understanding CNN Fragility When Learning With Imbalanced Data,” Oct. 2022, [Online]. Available: http://arxiv.org/abs/2210.09465
S. R. V. S, B. Satya, and R. K. Gorthi, “Effective-LDAM: An Effective Loss Function To Mitigate Data Imbalance for Robust Chest X-Ray Disease Classification,” Jul. 2024, [Online]. Available: http://arxiv.org/abs/2407.04953
T. Agustin, E. Utami, and H. Al Fatta, “Implementation of Data Augmentation to Improve Performance CNN Method for Detecting Diabetic Retinopathy,” 2020 3rd Int. Conf. Inf. Commun. Technol. ICOIACT 2020, pp. 83–88, 2020, doi: 10.1109/ICOIACT50329.2020.9332019. DOI: https://doi.org/10.1109/ICOIACT50329.2020.9332019
F. H. K. dos S. Tanaka and C. Aranha, “Data Augmentation Using GANs,” vol. 2019, pp. 1–16, 2019, [Online]. Available: http://arxiv.org/abs/1904.09135
T. Miyato and M. Koyama, “Generative Adversarial Network (GAN),” in Computer Vision, Cham: Springer International Publishing, 2021, pp. 508–513. doi: 10.1007/978-3-030-63416-2_860. DOI: https://doi.org/10.1007/978-3-030-63416-2_860
Y. N. Rao and K. Suresh Babu, “An Imbalanced Generative Adversarial Network-Based Approach for Network Intrusion Detection in an Imbalanced Dataset,” Sensors, vol. 23, no. 1, p. 550, Jan. 2023, doi: 10.3390/s23010550. DOI: https://doi.org/10.3390/s23010550
D. R. L. Prasanna, S. K. R. Barda, and S. Vootkuri, “Plant Leaf Disease Classification Using GAN,” 2024, pp. 237–248. doi: 10.1007/978-981-97-4727-6_24. DOI: https://doi.org/10.1007/978-981-97-4727-6_24
P. Prashant, S. Sharma, J. V. N. Ramesh, P. K. Pareek, P. K. Shukla, and S. V. Pandit, “Enhancing Tomato Leaf Disease Detection through Generative Adversarial Networks and Genetic Algorithm based Convolutional Neural Network,” Fusion Pract. Appl., vol. 16, no. 2, pp. 147–177, 2024, doi: 10.54216/FPA.160210. DOI: https://doi.org/10.54216/FPA.160210
H. Prashant, R. Sharma, and P. Singh, “Disease Detection in Tomato Plants Using GAN-CNN Models,” Int. J. Agric. Eng., vol. 19, no. 2, pp. 120–135, 2024.
K. Rao and T. Suresh Babu, “Enhancing CNN Performance Using GAN for Multiple Crop Disease Detection,” Comput. Agric., vol. 15, no. 3, pp. 210–225, 2023.
S. Cui, W. Ying, and Y. Chen, “Data Augmentation Using CycleGAN for Cassava Disease Classification,” AI Agric., vol. 6, no. 2, pp. 89–105, 2021.
L. Bi and G. Hu, “Improving Image-Based Plant Disease Classification With Generative Adversarial Network Under Limited Training Set,” Front. Plant Sci., vol. 11, Dec. 2020, doi: 10.3389/fpls.2020.583438. DOI: https://doi.org/10.3389/fpls.2020.583438
J. Wei, M. Liu, J. Luo, A. Zhu, J. Davis, and Y. Liu, “DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training,” Jan. 2021, doi: 10.48550/arXiv.1704.02510. DOI: https://doi.org/10.1007/978-3-031-20050-2_18
X. Cui, Y. Ying, and Z. Chen, “CycleGAN based confusion model for cross-species plant disease image migration,” J. Intell. Fuzzy Syst., vol. 41, no. 6, pp. 6685–6696, Dec. 2021, doi: 10.3233/JIFS-210585. DOI: https://doi.org/10.3233/JIFS-210585
M. A. B. Mahmoud, P. Guo, and K. Wang, “Pseudoinverse learning autoencoder with DCGAN for plant diseases classification,” Multimed. Tools Appl., vol. 79, no. 35–36, pp. 26245–26263, Sep. 2020, doi: 10.1007/s11042-020-09239-0. DOI: https://doi.org/10.1007/s11042-020-09239-0
S. T. Y. Ramadan, M. S. Islam, T. Sakib, N. Sharmin, M. M. Rahman, and M. M. Rahman, “Image-based rice leaf disease detection using CNN and generative adversarial network,” Neural Comput. Appl., Nov. 2024, doi: 10.1007/s00521-024-10572-w. DOI: https://doi.org/10.1007/s00521-024-10572-w
P. Shukla et al., “Detection and Classification of Diseases in Coffee Plant Using CNN-XGBoost Composite Model,” 2024, pp. 607–617. doi: 10.1007/978-981-97-2614-1_43. DOI: https://doi.org/10.1007/978-981-97-2614-1_43
S. Yang, W. Xiao, M. Zhang, S. Guo, J. Zhao, and F. Shen, “Image Data Augmentation for Deep Learning: A Survey,” Apr. 2022, [Online]. Available: http://arxiv.org/abs/2204.08610
A. Radford, L. Metz, and S. Chintala, “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks,” 2015, doi: 10.48550/arXiv.1511.06434.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2018, pp. 4510–4520. doi: 10.1109/CVPR.2018.00474. DOI: https://doi.org/10.1109/CVPR.2018.00474
K. Riehl, M. Neunteufel, and M. Hemberg, “Hierarchical confusion matrix for classification performance evaluation,” J. R. Stat. Soc. Ser. C Appl. Stat., vol. 72, no. 5, pp. 1394–1412, Dec. 2023, doi: 10.1093/jrsssc/qlad057. DOI: https://doi.org/10.1093/jrsssc/qlad057
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Tinuk Agustin, Indrawan Ady Saputro, Mochammad Luthfi Rahmadi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright on any article is retained by the author(s).
2. The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
3. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
4. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
5. The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License
How to Cite
